import numpy as np

from datasets import load_dataset
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainingArguments,
)

MODEL_NAME = "distilbert-base-uncased"

LABELS = [
    "1.3.1 Info and Relationships",
    "2.1.1 Keyboard",
    "2.4.3 Focus Order",
    "2.4.7 Focus Visible",
    "4.1.2 Name, Role, Value",
]

label2id = {label: i for i, label in enumerate(LABELS)}
id2label = {i: label for i, label in enumerate(LABELS)}

# Load our CSV files
dataset = load_dataset(
    "csv",
    data_files={
        "train": "train.csv",
        "validation": "validation.csv",
        "test": "test.csv",
    },
)

# Load DistilBERT's tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)


def preprocess(example):
    encoded = tokenizer(
        example["text"],
        truncation=True,
    )

    encoded["label_id"] = label2id[example["label"]]

    return encoded


tokenized_dataset = dataset.map(
    preprocess,
    remove_columns=["text", "label"],
)

tokenized_dataset = tokenized_dataset.rename_column(
    "label_id",
    "labels",
)

# Pads each batch to the longest sentence in that batch
data_collator = DataCollatorWithPadding(
    tokenizer=tokenizer
)

# Load pretrained DistilBERT with our new 5-class head
model = AutoModelForSequenceClassification.from_pretrained(
    MODEL_NAME,
    num_labels=len(LABELS),
    id2label=id2label,
    label2id=label2id,
)


def compute_metrics(eval_pred):
    logits, labels = eval_pred

    predictions = np.argmax(logits, axis=-1)

    precision, recall, f1, _ = precision_recall_fscore_support(
        labels,
        predictions,
        average="weighted",
        zero_division=0,
    )

    accuracy = accuracy_score(
        labels,
        predictions,
    )

    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1,
    }


training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    learning_rate=2e-5,
    eval_strategy="epoch",
    save_strategy="epoch",
    logging_strategy="epoch",
    load_best_model_at_end=True,
    metric_for_best_model="f1",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["validation"],
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)

print("\nStarting training...\n")

trainer.train()

print("\nEvaluating final model on test data...\n")

test_results = trainer.evaluate(
    tokenized_dataset["test"]
)

print(test_results)

# Save our finished model locally
trainer.save_model("./wcag-classifier")
tokenizer.save_pretrained("./wcag-classifier")